Désirée Baumann

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The prediction of operons in Mycobacterium tuberculosis (MTB) is a first step toward understanding the regulatory network of this pathogen. Here we apply a statistical model using logistic regression to predict operons in MTB. As predictors, our model incorporates intergenic distance and the correlation of gene expression calculated for adjacent gene pairs(More)
Portable point-of-care devices for pathogen detection require easy, minimal and user-friendly handling steps and need to have the same diagnostic performance compared to centralized laboratories. In this work we present a fully automated sample-to-answer detection of influenza A H3N2 virus in a centrifugal LabDisk with complete prestorage of reagents. Thus,(More)
Background: Generally, QSAR modelling requires both model selection and validation since there is no a priori knowledge about the optimal QSAR model. Prediction errors (PE) are frequently used to select and to assess the models under study. Reliable estimation of prediction errors is challenging – especially under model uncertainty – and requires(More)
In most cases of QSAR modelling the final model used to make predictions, is not known a priori but has to be selected in a data driven fashion (e.g. selection of principal components, variable selection, selection of the best mathematical modelling technique). Reliable estimation of externally validated prediction errors under this model uncertainty is(More)
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